Towards optimal head-to-head autonomous racing with curriculum reinforcement learning

D Kalaria, Q Lin, JM Dolan - arxiv preprint arxiv:2308.13491, 2023 - arxiv.org
Head-to-head autonomous racing is a challenging problem, as the vehicle needs to operate
at the friction or handling limits in order to achieve minimum lap times while also actively …

Learning-Based On-Track System Identification for Scaled Autonomous Racing in Under a Minute

O Dikici, E Ghignone, C Hu, N Baumann… - IEEE Robotics and …, 2025 - ieeexplore.ieee.org
Accurate tire modeling is crucial for optimizing autonomous racing vehicles, as State-of-the-
Art (SotA) modelbased techniques rely on precise knowledge of the vehicle's parameters …

Autonomous Drifting Based on Maximal Safety Probability Learning

H Hoshino, J Li, A Menon, JM Dolan… - arxiv preprint arxiv …, 2024 - arxiv.org
This paper proposes a novel learning-based framework for autonomous driving based on
the concept of maximal safety probability. Efficient learning requires rewards that are …

Real-Time Algorithms for Game-Theoretic Motion Planning and Control in Autonomous Racing using Near-Potential Function

D Kalaria, C Maheshwari, S Sastry - arxiv preprint arxiv:2412.08855, 2024 - arxiv.org
Autonomous racing extends beyond the challenge of controlling a racecar at its physical
limits. Professional racers employ strategic maneuvers to outwit other competing opponents …

Disturbance Observer-based Control Barrier Functions with Residual Model Learning for Safe Reinforcement Learning

D Kalaria, Q Lin, JM Dolan - arxiv preprint arxiv:2410.06570, 2024 - arxiv.org
Reinforcement learning (RL) agents need to explore their environment to learn optimal
behaviors and achieve maximum rewards. However, exploration can be risky when training …